Lack of racial and ethnic disparities in mortality in minority patients hospitalised with COVID-19 in a mid-Atlantic healthcare system.

INTRODUCTION: In the USA, minoritised communities (racial and ethnic) have suffered disproportionately from COVID-19 compared with non-Hispanic white communities. In a large cohort of patients hospitalised for COVID-19 in a healthcare system spanning five adult hospitals, we analysed outcomes of patients based on race and ethnicity. METHODS: This was a retrospective cohort analysis of patients 18 years or older admitted to five hospitals in the mid-Atlantic area between 4 March 2020 and 27 May 2022 with confirmed COVID-19. Participants were divided into four groups based on their race/ethnicity: non-Hispanic black, non-Hispanic white, Latinx and other. Propensity score weighted generalised linear models were used to assess the association between race/ethnicity and the primary outcome of in-hospital mortality. RESULTS: Of the 9651 participants in the cohort, more than half were aged 18-64 years old (56%) and 51% of the cohort were females. Non-Hispanic white patients had higher mortality (p<0.001) and longer hospital length-of-stay (p<0.001) than Latinx and non-Hispanic black patients. DISCUSSION: In this large multihospital cohort of patients admitted with COVID-19, non-Hispanic black and Hispanic patients did not have worse outcomes than white patients. Such findings likely reflect how the complex range of factors that resulted in a life-threatening and disproportionate impact of incidence on certain vulnerable populations by COVID-19 in the community was offset through admission at well-resourced hospitals and healthcare systems. However, there continues to remain a need for efforts to address the significant pre-existing race and ethnicity inequities highlighted by the COVID-19 pandemic to be better prepared for future public health emergencies.

Racial Inequities in the Control of Hypertension and the Explanatory Role of Residential Segregation: a Decomposition Analysis in the Brazilian Longitudinal Study of Adult Health (ELSA-Brasil).

The mechanisms underlying racial inequities in uncontrolled hypertension have been limited to individual factors. We investigated racial inequities in uncontrolled hypertension and the explanatory role of economic segregation in the Brazilian Longitudinal Study of Adult Health (ELSA-Brasil). All 3897 baseline participants with hypertension (2008-2010) were included. Uncontrolled hypertension (SBP ≥ 140 mmHg or DBP ≥ 90 mmHg), self-reported race (White/Brown/Black people), and neighborhood economic segregation (low/medium/high) were analyzed cross-sectionally. We used decomposition analysis, which describes how much a disparity would change (disparity reduction; explained portion) and remain (disparity residual; unexplained portion) upon removing racial differences in economic segregation (i.e., if Black people had the distribution of segregation of White people, how much we would expect uncontrolled hypertension to decrease among Black people). Age- and gender-adjusted prevalence of uncontrolled hypertension (39.0%, 52.6%, and 54.2% for White, Brown, and Black participants, respectively) remained higher for Black and Brown vs White participants, regardless of economic segregation. Uncontrolled hypertension showed a dose-response pattern with increasing segregation levels for White but not for Black and Brown participants. After adjusting for age, gender, education, and study center, unexplained portion (disparity residual) of race on uncontrolled hypertension was 18.2% (95% CI 13.4%; 22.9%) for Black vs White participants and 12.6% (8.2%; 17.1%) for Brown vs White participants. However, explained portion (disparity reduction) through economic segregation was - 2.1% (- 5.1%; 1.3%) for Black vs White and 0.5% (- 1.7%; 2.8%) for Brown vs White participants. Although uncontrolled hypertension was greater for Black and Brown vs White people, racial inequities in uncontrolled hypertension were not explained by economic segregation.

The Importance of Equity Value Judgments and Estimator-Estimand Alignment in Measuring Disparity and Identifying Targets to Reduce Disparity.

The choice of which covariates to adjust for (so-called allowability designation (AD)) in health disparity measurements reflects value judgments about inequitable versus equitable sources of health differences, which is paramount for making inferences about disparity. Yet, many off-the-shelf estimators used in health disparity research are not designed with equity considerations in mind, and they imply different ADs. We demonstrated the practical importance of incorporating equity concerns in disparity measurements through simulations, motivated by the example of reducing racial disparities in hypertension control via interventions on disparities in treatment intensification. Seven causal decomposition estimators, each with a particular AD (with respect to disparities in hypertension control and treatment intensification), were considered to estimate the observed outcome disparity and the reduced/residual disparity under the intervention. We explored the implications for bias of the mismatch between equity concerns and the AD in the estimator under various causal structures (through altering racial differences in covariates or the confounding mechanism). The estimator that correctly reflects equity concerns performed well under all scenarios considered, whereas the other estimators were shown to have the risk of yielding large biases in certain scenarios, depending on the interaction between their ADs and the specific causal structure.

Geospatial Distribution of Racial Disparities in Influenza Vaccination in Nursing Homes.

OBJECTIVES: This study aimed to assess the distribution of racial disparities in influenza vaccination between White and Black short-stay and long-stay nursing home residents among states and hospital referral regions (HRRs). DESIGN: Retrospective cohort study. SETTING AND PARTICIPANTS: We included short-stay and long-stay older adults residing in US nursing homes during influenza seasons between 2011 and 2018. Included residents were aged ≥65 years and enrolled in Traditional Medicare. Analyses were conducted using resident-seasons, whereby residents could contribute to one or more influenza seasons if they resided in a nursing home across multiple seasons. METHODS: Our comparison of interest was marginalized vs privileged racial group membership measured as Black vs White race. We obtained influenza vaccination documentation from resident Minimum Data Set assessments from October 1 through June 30 of a particular influenza season. Nonparametric g-formula was used to estimate age- and sex-standardized disparities in vaccination, measured as the percentage point (pp) difference in the proportions of individuals vaccinated between Black and White nursing home residents within states and HRRs. RESULTS: The study included 7,807,187 short-stay resident-seasons (89.7% White and 10.3% Black) in 14,889 nursing homes and 7,308,111 long-stay resident-seasons (86.7% White and 13.3% Black) in 14,885 nursing homes. Among states, the median age- and sex-standardized disparity between Black and White residents was 10.1 percentage points (pps) among short-stay residents and 5.3 pps among long-stay residents across seasons. Among HRRs, the median disparity was 8.6 pps among short-stay residents and 5.0 pps among long-stay residents across seasons. CONCLUSIONS AND IMPLICATIONS: Our analysis revealed that the magnitudes of vaccination disparities varied substantially across states and HRRs, from no disparity in vaccination to disparities in excess of 25 pps. Local interventions and policies should be targeted to high-disparity geographic areas to increase vaccine uptake and promote health equity.

Artificial intelligence-based clinical decision support for liver transplant evaluation and considerations about fairness: A qualitative study.

BACKGROUND: The use of large-scale data and artificial intelligence (AI) to support complex transplantation decisions is in its infancy. Transplant candidate decision-making, which relies heavily on subjective assessment (ie, high variability), provides a ripe opportunity for AI-based clinical decision support (CDS). However, AI-CDS for transplant applications must consider important concerns regarding fairness (ie, health equity). The objective of this study was to use human-centered design methods to elicit providers' perceptions of AI-CDS for liver transplant listing decisions. METHODS: In this multicenter qualitative study conducted from December 2020 to July 2021, we performed semistructured interviews with 53 multidisciplinary liver transplant providers from 2 transplant centers. We used inductive coding and constant comparison analysis of interview data. RESULTS: Analysis yielded 6 themes important for the design of fair AI-CDS for liver transplant listing decisions: (1) transparency in the creators behind the AI-CDS and their motivations; (2) understanding how the AI-CDS uses data to support recommendations (ie, interpretability); (3) acknowledgment that AI-CDS could mitigate emotions and biases; (4) AI-CDS as a member of the transplant team, not a replacement; (5) identifying patient resource needs; and (6) including the patient's role in the AI-CDS. CONCLUSIONS: Overall, providers interviewed were cautiously optimistic about the potential for AI-CDS to improve clinical and equitable outcomes for patients. These findings can guide multidisciplinary developers in the design and implementation of AI-CDS that deliberately considers health equity.

Geographic Variation in Racial Disparities in Receipt of High-Dose Influenza Vaccine Among US Older Adults.

BACKGROUND: Racial disparities in receipt of high-dose influenza vaccine (HDV) have been documented nationally, but whether small-area geographic variation in such disparities exists remains unknown. We assessed the distribution of disparities in HDV receipt between Black and White traditional Medicare beneficiaries vaccinated against influenza within states and hospital referral regions (HRRs). METHODS: We conducted a nationally representative retrospective cohort study of 11,768,724 community-dwelling traditional Medicare beneficiaries vaccinated against influenza during the 2015-2016 influenza season (94.3% White and 5.7% Black). Our comparison was marginalized versus privileged racial group measured as Black versus White race. Vaccination and type of vaccine were obtained from Medicare Carrier and Outpatient files. Differences in the proportions of individuals who received HDV between Black and White beneficiaries within states and HRRs were used to measure age- and sex-standardized disparities in HDV receipt. We restricted to states and HRRs with ≥ 100 beneficiaries per age-sex strata per racial group. RESULTS: We detected a national disparity in HDV receipt of 12.8 percentage points (pps). At the state level, the median standardized HDV receipt disparity was 10.7 pps (minimum, maximum: 2.9, 25.6; n = 30 states). The median standardized HDV receipt disparity among HRRs was 11.6 pps (minimum, maximum: 0.4, 24.7; n = 54 HRRs). CONCLUSION: Black beneficiaries were less likely to receive HDV compared to White beneficiaries in almost every state and HRR in our analysis. The magnitudes of disparities varied substantially across states and HRRs. Local interventions and policies are needed to target geographic areas with the largest disparities to address these inequities.

Critical interactions between race and the highly granular area deprivation index in liver transplant evaluation.

Neighborhood socioeconomic deprivation may have important implications on disparities in liver transplant (LT) evaluation. In this retrospective cohort study, we constructed a novel dataset by linking individual patient-level data with the highly granular Area Deprivation Index (ADI), which is advantageous over other neighborhood measures due to: specificity of Census Block-Group (versus Census Tract, Zip code), scoring, and robust variables. Our cohort included 1377 adults referred to our center for LT evaluation 8/1/2016-12/31/2019. Using modified Poisson regression, we tested for effect measure modification of the association between neighborhood socioeconomic status (nSES) and LT evaluation outcomes (listing, initiating evaluation, and death) by race and ethnicity. Compared to patients with high nSES, those with low nSES were at higher risk of not being listed (aRR = 1.14; 95%CI 1.05-1.22; p < .001), of not initiating evaluation post-referral (aRR = 1.20; 95%CI 1.01-1.42; p = .03) and of dying without initiating evaluation (aRR = 1.55; 95%CI 1.09-2.2; p = .01). While White patients with low nSES had similar rates of listing compared to White patients with high nSES (aRR = 1.06; 95%CI .96-1.17; p = .25), Underrepresented patients from neighborhoods with low nSES incurred 31% higher risk of not being listed compared to Underrepresented patients from neighborhoods with high nSES (aRR = 1.31; 95%CI 1.12-1.5; p < .001). Interventions addressing neighborhood deprivation may not only benefit patients with low nSES but may address racial and ethnic inequities.

Comparative effectiveness of omalizumab, mepolizumab, and dupilumab in asthma: A target trial emulation.

BACKGROUND: Multiple mAbs are currently approved for the treatment of asthma. However, there is limited evidence on their comparative effectiveness. OBJECTIVE: Our aim was to compare the effectiveness of omalizumab, mepolizumab, and dupilumab in individuals with moderate-to-severe asthma. METHODS: We emulated a hypothetical randomized trial using electronic health records from a large US-based academic health care system. Participants aged 18 years or older with baseline IgE levels between 30 and 700 IU/mL and peripheral eosinophil counts of at least 150 cells/µL were eligible for study inclusion. The study period extended from March 2016 to August 2021. Outcomes included the incidence of asthma-related exacerbations and change in baseline FEV1 value over 12 months of follow-up. RESULTS: In all, 68 individuals receiving dupilumab, 68 receiving omalizumab, and 65 receiving mepolizumab met the inclusion criteria. Over 12 months of follow-up, 31 exacerbations occurred over 68 person years (0.46 exacerbations per person year) in the dupilumab group, 63 over 68 person years (0.93 per person year) in the omalizumab group, and 86 over 65 person years (1.32 per person year) in the mepolizumab group (adjusted incidence rate ratios: dupilumab vs mepolizumab, 0.28 [95% CI = 0.09-0.84]; dupilumab vs omalizumab, 0.36 [95% CI = 0.12-1.09]; and omalizumab vs mepolizumab, 0.78 [95% CI = 0.32-1.91]). The differences in the change in FEV1 comparing patients who received the different biologics were as follows: 0.11 L (95% CI = -0.003 to 0.222 L) for dupilumab versus mepolizumab, 0.082 L (95% CI -0.040 to 0.204 L) for dupilumab versus omalizumab, and 0.026 L (95% CI -0.083 to 0.140 L) for omalizumab versus mepolizumab. CONCLUSIONS: Among patients with asthma and eosinophil counts of at least 150 cells/µL and IgE levels of 30 to 700 kU/L, dupilumab was associated with greater improvements in exacerbation and FEV1 value than omalizumab and mepolizumab.

An intersectional analysis of historical and contemporary structural racism on non-fatal shootings in Baltimore, Maryland.

Introduction Non-fatal shooting rates vary tremendously within cities in the USA. Factors related to structural racism (both historical and contemporary) could help explain differences in non-fatal shooting rates at the neighbourhood level. Most research assessing the relationship between structural racism and firearm violence only includes one dimension of structural racism. Our study uses an intersectional approach to examine how the interaction of two forms of structural racism is associated with spatial non-fatal shooting disparities in Baltimore, Maryland. Methods We present three additive interaction measures to describe the relationship between historical redlining and contemporary racialized economic segregation on neighbourhood-level non-fatal shootings. Results Our findings revealed that sustained disadvantage census tracts (tracts that experience contemporary socioeconomic disadvantage and were historically redlined) have the highest burden of non-fatal shootings. Sustained disadvantage tracts had on average 24 more non-fatal shootings a year per 10 000 residents compared with similarly populated sustained advantage tracts (tracts that experience contemporary socioeconomic advantage and were not historically redlined). Moreover, we found that between 2015 and 2019, the interaction between redlining and racialized economic segregation explained over one-third of non-fatal shootings (approximately 650 shootings) in sustained disadvantage tracts. Conclusion These findings suggest that the intersection of historical and contemporary structural racism is a fundamental cause of firearm violence inequities in Baltimore. Intersectionality can advance injury prevention research and practice by (1) serving as an analytical tool to expose inequities in injury-related outcomes and (2) informing the development and implementation of injury prevention interventions and policies that prioritise health equity and racial justice.

Flexible propensity score estimation strategies for clustered data in observational studies.

Existing studies have suggested superior performance of nonparametric machine learning over logistic regression for propensity score estimation. However, it is unclear whether the advantages of nonparametric propensity score modeling are carried to settings where there is clustering of individuals, especially when there is unmeasured cluster-level confounding. In this work we examined the performance of logistic regression (all main effects), Bayesian additive regression trees and generalized boosted modeling for propensity score weighting in clustered settings, with the clustering being accounted for by including either cluster indicators or random intercepts. We simulated data for three hypothetical observational studies of varying sample and cluster sizes. Confounders were generated at both levels, including a cluster-level confounder that is unobserved in the analyses. A binary treatment and a continuous outcome were generated based on seven scenarios with varying relationships between the treatment and confounders (linear and additive, nonlinear/nonadditive, nonadditive with the unobserved cluster-level confounder). Results suggest that when the sample and cluster sizes are large, nonparametric propensity score estimation may provide better covariate balance, bias reduction, and 95% confidence interval coverage, regardless of the degree of nonlinearity or nonadditivity in the true propensity score model. When the sample or cluster sizes are small, however, nonparametric approaches may become more vulnerable to unmeasured cluster-level confounding and thus may not be a better alternative to multilevel logistic regression. We applied the methods to the National Longitudinal Study of Adolescent to Adult Health data, estimating the effect of team sports participation during adolescence on adulthood depressive symptoms.

Scaling Interventions to Manage Chronic Disease: Innovative Methods at the Intersection of Health Policy Research and Implementation Science.

Policy implementation is a key component of scaling effective chronic disease prevention and management interventions. Policy can support scale-up by mandating or incentivizing intervention adoption, but enacting a policy is only the first step. Fully implementing a policy designed to facilitate implementation of health interventions often requires a range of accompanying implementation structures, like health IT systems, and implementation strategies, like training. Decision makers need to know what policies can support intervention adoption and how to implement those policies, but to date research on policy implementation is limited and innovative methodological approaches are needed. In December 2021, the Johns Hopkins ALACRITY Center for Health and Longevity in Mental Illness and the Johns Hopkins Center for Mental Health and Addiction Policy convened a forum of research experts to discuss approaches for studying policy implementation. In this report, we summarize the ideas that came out of the forum. First, we describe a motivating example focused on an Affordable Care Act Medicaid health home waiver policy used by some US states to support scale-up of an evidence-based integrated care model shown in clinical trials to improve cardiovascular care for people with serious mental illness. Second, we define key policy implementation components including structures, strategies, and outcomes. Third, we provide an overview of descriptive, predictive and associational, and causal approaches that can be used to study policy implementation. We conclude with discussion of priorities for methodological innovations in policy implementation research, with three key areas identified by forum experts: effect modification methods for making causal inferences about how policies' effects on outcomes vary based on implementation structures/strategies; causal mediation approaches for studying policy implementation mechanisms; and characterizing uncertainty in systems science models. We conclude with discussion of overarching methods considerations for studying policy implementation, including measurement of policy implementation, strategies for studying the role of context in policy implementation, and the importance of considering when establishing causality is the goal of policy implementation research.

Recommendations for Using Causal Diagrams to Study Racial Health Disparities.

There have been calls for race to be denounced as a biological variable and for a greater focus on racism, instead of solely race, when studying racial health disparities in the United States. These calls are grounded in extensive scholarship and the rationale that race is not a biological variable, but instead socially constructed, and that structural/institutional racism is a root cause of race-related health disparities. However, there remains a lack of clear guidance for how best to incorporate these assertions about race and racism into tools, such as causal diagrams, that are commonly used by epidemiologists to study population health. We provide clear recommendations for using causal diagrams to study racial health disparities that were informed by these calls. These recommendations consider a health disparity to be a difference in a health outcome that is related to social, environmental, or economic disadvantage. We present simplified causal diagrams to illustrate how to implement our recommendations. These diagrams can be modified based on the health outcome and hypotheses, or for other group-based differences in health also rooted in disadvantage (e.g., gender). Implementing our recommendations may lead to the publication of more rigorous and informative studies of racial health disparities.

Comparative efficacy of mepolizumab, benralizumab, and dupilumab in eosinophilic asthma: A Bayesian network meta-analysis.

BACKGROUND: The comparative safety and efficacy of the biologics currently approved for asthma are unclear. OBJECTIVE: We compared the safety and efficacy of mepolizumab, benralizumab, and dupilumab in individuals with severe eosinophilic asthma. METHODS: We performed a systematic review of peer-reviewed literature published 2000 to 2021. We studied Bayesian network meta-analyses of exacerbation rates, prebronchodilator FEV1, the Asthma Control Questionnaire, and serious adverse events in individuals with eosinophilic asthma. RESULTS: Eight randomized clinical trials (n = 6461) were identified. We found in individuals with eosinophils ≥300 cells/µL the following: in reducing exacerbation rates compared to placebo: dupilumab (risk ratio [RR], 0.32; 95% credible interval [CI], 0.23 to 0.45), mepolizumab (RR, 0.37; 95% CI, 0.30 to 0.45), and benralizumab (RR, 0.49; 95% CI, 0.43 to 0.55); in improving FEV1: dupilumab (mean difference in milliliters [MD] 230; 95% CI, 160 to 300), benralizumab (MD, 150; 95% CI, 100 to 200), and mepolizumab (MD, 150; 95% CI, 66 to 220); and in reducing Asthma Control Questionnaire scores: mepolizumab (MD, -0.63; 95% CI, -0.81 to -0.45), dupilumab (MD, -0.48; 95% CI, -0.83 to -0.14), and benralizumab (MD, -0.32; 95% CI, -0.43 to -0.21). In individuals with eosinophils 150-299 cells/µL, benralizumab (RR, 0.62; 95% CI, 0.52 to 0.73) and dupilumab (RR, 0.60; 95% CI, 0.38 to 0.95) were associated with lower exacerbation rates; and only benralizumab (MD, 81; 95% CI, 8 to 150) significantly improved FEV1. These differences were minimal compared to clinically important thresholds. For serious adverse events in the overall population, mepolizumab (odds ratio, 0.67; 95% CI, 0.48 to 0.92) and benralizumab (odds ratio, 0.74; 95% CI, 0.59 to 0.93) were associated with lower odds of a serious adverse event, while dupilumab was not different from placebo (odds ratio, 1.0; 95% CI, 0.74 to 1.4). CONCLUSION: There are minimal differences in the efficacy and safety of mepolizumab, benralizumab, and dupilumab in eosinophilic asthma.

Perceived racial discrimination partially mediates racial-ethnic disparities in dental utilization and oral health.

OBJECTIVES: Perceived racial discrimination has been associated with poor health outcomes, yet its impact on oral health disparities is not understood. We examine the role of perceived racial discrimination in healthcare settings in explaining racial-ethnic disparities in dental visits and tooth loss. METHODS: We used behavioral risk factor surveillance system (BRFSS) data for 2014 from four diverse states (AZ, MN, MS and NM) that included "reactions to race" module. Using Poisson regression to estimate risk ratios, we employed inverse odds ratio(IOR)-weighted estimation for mediation analyses to estimate the role of perceived discrimination, after equalizing other confounders and risk factors. RESULTS: We found that among those with similar risk factors, those who experienced racial discrimination were 15% less likely to visit a dentist, and 12% more likely to have tooth loss than those who were treated same as other races. Both Hispanics and non-Hispanic Blacks were 26% less likely to visit a dentist (for Hispanics, RR = 0.74, 95%CI: 0.69-0.78; for non-Hispanic Blacks, RR = 0.74, 95%CI: 0.70-0.79), and non-Hispanic Blacks were 36% more likely to have tooth loss relative to non-Hispanic Whites with similar risk factors (RR = 1.36, 95%CI: 1.28-1.45). Perceived discrimination appears to contribute to racial-ethnic disparities in dental utilization among Hispanics, and disparities in tooth loss among non-Hispanic Blacks, relative to non-Hispanic Whites. CONCLUSIONS: Perceived racial discrimination partially explains the racial-ethnic disparities in dental utilization and tooth loss among those who otherwise share the same risk factors for these outcomes. Addressing discrimination and systemic racism can reduce the racial-ethnic disparities in oral health.

Multicenter study of racial and ethnic inequities in liver transplantation evaluation: Understanding mechanisms and identifying solutions.

Racial and ethnic disparities persist in access to the liver transplantation (LT) waiting list; however, there is limited knowledge about underlying system-level factors that may be responsible for these disparities. Given the complex nature of LT candidate evaluation, a human factors and systems engineering approach may provide insights. We recruited participants from the LT teams (coordinators, advanced practice providers, physicians, social workers, dieticians, pharmacists, leadership) at two major LT centers. From December 2020 to July 2021, we performed ethnographic observations (participant-patient appointments, committee meetings) and semistructured interviews (N = 54 interviews, 49 observation hours). Based on findings from this multicenter, multimethod qualitative study combined with the Systems Engineering Initiative for Patient Safety 2.0 (a human factors and systems engineering model for health care), we created a conceptual framework describing how transplant work system characteristics and other external factors may improve equity in the LT evaluation process. Participant perceptions about listing disparities described external factors (e.g., structural racism, ambiguous national guidelines, national quality metrics) that permeate the LT evaluation process. Mechanisms identified included minimal transplant team diversity, implicit bias, and interpersonal racism. A lack of resources was a common theme, such as social workers, transportation assistance, non-English-language materials, and time (e.g., more time for education for patients with health literacy concerns). Because of the minimal data collection or center feedback about disparities, participants felt uncomfortable with and unadaptable to unwanted outcomes, which perpetuate disparities. We proposed transplant center-level solutions (i.e., including but not limited to training of staff on health equity) to modifiable barriers in the clinical work system that could help patient navigation, reduce disparities, and improve access to care. Our findings call for an urgent need for transplant centers, national societies, and policy makers to focus efforts on improving equity (tailored, patient-centered resources) using the science of human factors and systems engineering.

Association Between Patient-Clinician Relationships and Adherence to Antihypertensive Medications Among Black Adults: An Observational Study Design.

Background We assessed the associations between patient-clinician relationships (communication and involvement in shared decision-making [SDM]) and adherence to antihypertensive medications. Methods and Results The 2010 to 2017 Medical Expenditure Panel Survey (MEPS) data were analyzed. A retrospective cohort study design was used to create a cohort of prevalent and new users of antihypertensive medications. We defined constructs of patient-clinician communication and involvement in SDM from patient responses to the standard questionnaires about satisfaction and access to care during the first year of surveys. Verified self-reported medication refill information collected during the second year of surveys was used to calculate medication refill adherence; adherence was defined as medication refill adherence ≥80%. Survey-weighted multivariable-adjusted logistic regression models were used to measure the odds ratio (OR) and 95% CI for the association between both patient-clinician constructs and adherence. Our analysis involved 2571 Black adult patients with hypertension (mean age of 58 years; SD, 14 years) who were either persistent (n=1788) or new users (n=783) of antihypertensive medications. Forty-five percent (n=1145) and 43% (n=1016) of the sample reported having high levels of communication and involvement in SDM, respectively. High, versus low, patient-clinician communication (OR, 1.38; 95% CI, 1.14-1.67) and involvement in SDM (OR, 1.32; 95% CI, 1.08-1.61) were both associated with adherence to antihypertensives after adjusting for multiple covariates. These associations persisted among a subgroup of new users of antihypertensive medications. Conclusions Patient-clinician communication and involvement in SDM are important predictors of optimal adherence to antihypertensive medication and should be targeted for improving adherence among Black adults with hypertension.

Meaningful Causal Decompositions in Health Equity Research: Definition, Identification, and Estimation Through a Weighting Framework.

Causal decomposition analyses can help build the evidence base for interventions that address health disparities (inequities). They ask how disparities in outcomes may change under hypothetical intervention. Through study design and assumptions, they can rule out alternate explanations such as confounding, selection bias, and measurement error, thereby identifying potential targets for intervention. Unfortunately, the literature on causal decomposition analysis and related methods have largely ignored equity concerns that actual interventionists would respect, limiting their relevance and practical value. This article addresses these concerns by explicitly considering what covariates the outcome disparity and hypothetical intervention adjust for (so-called allowable covariates) and the equity value judgments these choices convey, drawing from the bioethics, biostatistics, epidemiology, and health services research literatures. From this discussion, we generalize decomposition estimands and formulae to incorporate allowable covariate sets (and thereby reflect equity choices) while still allowing for adjustment of non-allowable covariates needed to satisfy causal assumptions. For these general formulae, we provide weighting-based estimators based on adaptations of ratio-of-mediator-probability and inverse-odds-ratio weighting. We discuss when these estimators reduce to already used estimators under certain equity value judgments, and a novel adaptation under other judgments.